Предоставьте своему ИИ-помощнику по программированию мгновенный доступ к полной документации IMAGIN.studio — конфигурации CDN, справочникам API, руководствам по интеграции и многому другому.
Один инструмент. Одна команда. Работает со всеми основными ИИ-помощниками по программированию.
Вставьте это в конфигурацию MCP вашего агента:
{
"mcpServers": {
"imagin-docs": {
"command": "uvx",
"args": ["imagin-studio-api-docs-mcp"]
}
}
}Или просто попросите своего ИИ-помощника:
Установи этот MCP-сервер: https://pypi.org/project/imagin-studio-api-docs-mcp/
Related MCP server: @ragrabbit/mcp
Установка — uvx imagin-studio-api-docs-mcp (без клонирования, без venv, без настройки)
Индексация — При первом запуске клонирует документацию и создает локальный векторный индекс (~30 сек)
Поиск — Ваш ИИ-помощник вызывает search_docs для поиска соответствующей документации
Актуальность — Индекс автоматически обновляется при изменении исходной документации
Все работает локально. Никаких API-ключей. Никаких внешних сервисов.
Агент | Расположение конфигурации |
Claude Code |
|
Claude Desktop |
|
Cursor | Настройки > Инструменты и MCP |
Windsurf |
|
VS Code + Copilot |
|
Cline | Панель MCP-серверов |
Zed |
|
Подробные инструкции по настройке для каждого агента см. в полном руководстве по установке на PyPI.
Если ваш агент не может найти uvx (часто встречается в графических приложениях, таких как Claude Desktop и Cursor):
{
"mcpServers": {
"imagin-docs": {
"command": "npx",
"args": ["-y", "@imagin.studio/api-docs-mcp"]
}
}
}После установки попробуйте такие запросы:
"Найди в документации IMAGIN информацию об очистке кэша CDN"
"Как настроить собственный домен с IMAGIN?"
"Найди API-эндпоинт для трансформации изображений"
"Какие форматы изображений поддерживает IMAGIN.studio?"
Реестр | Пакет | Установка |
PyPI |
| |
npm |
|
Apache License 2.0 — см. LICENSE.
Search the official IMAGIN.studio technical documentation, integration guides, and knowledge base.
Use this tool when the user asks 'How do I...' questions, needs explanation on API concepts (CDN, referrers, caching, 360 spinner), or needs to debug integration issues. Rewrite vague queries into specific technical search terms before calling.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language search query. Use specific technical terms rather than vague descriptions. Good: "CDN cache invalidation headers". Bad: "caching stuff". | |
| top_k | No | Number of results to return (1-20, default 5). Use 1-3 for focused lookups, 5 for general questions, 10-20 for broad research. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint true, idempotentHint true, destructiveHint false. The description adds that it searches specific content types but does not discuss rate limits, authentication, or result format. It adds some context but not rich behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, no redundant words. First sentence states purpose, second gives usage examples, third provides query rewriting advice. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only search tool with good annotations and output schema, the description covers when to use and what to search. It could mention authentication scope or result limitations, but overall it's reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with detailed descriptions for both parameters. The description does not add per-parameter details beyond the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches official IMAGIN.studio technical documentation, integration guides, and knowledge base. This is specific and complete, with no sibling tools to distinguish from.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says when to use ('How do I' questions, API concepts, debug issues) and provides guidance to rewrite vague queries into specific terms. This is optimal usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.23search_docsWith only one tool, there is no possibility of confusion between different tools. The single tool is clearly described for its specific purpose.
The single tool name 'search_docs' follows a consistent verb_noun pattern, which is clear and predictable.
One tool is minimal and may feel insufficient for a documentation set; however, it might be acceptable if the scope is strictly limited to search. Still, it falls into the 'thin' category.
The tool covers the core search functionality, but lacks additional operations like retrieving a specific document or listing available topics, which could be useful for a documentation server.
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